Unit information: Uncertainty Modelling for Intelligent Systems in 2018/19

Unit name	Uncertainty Modelling for Intelligent Systems
Unit code	EMATM1120
Credit points	10
Level of study	M/7
Teaching block(s)	Teaching Block 1 (weeks 1 - 12)
Unit director	Professor. Lawry
Open unit status	Not open
Pre-requisites	EMAT10704 Discrete Mathematics 1, or equivalent.
Co-requisites	None
School/department	School of Engineering Mathematics and Technology
Faculty	Faculty of Engineering

Description including Unit Aims

This unit will explore the techniques and methodologies developed within Artificial Intelligence to represent and reason with information which is uncertain, imprecise or fuzzy. The unit will provide an overview of a range of different approaches explaining both the mathematics and the underlying philosophy and investigating practical applications.

Aims:

• To provide students with an overview of uncertainty modelling techniques and formalisms

• To provide students with an in-depth study of the mathematics and philosophy underlying these techniques

• To provide a detailed analysis of the application of uncertainty modelling in intelligent systems.

Intended Learning Outcomes

1. To give an understanding of what approaches are available in uncertainty modelling, and under what conditions they can be appropriately applied.
2. To give students familiarity with the advanced mathematics underlying different approaches to uncertainty modelling.
3. To provide insight into the practical application of uncertainty modelling

Teaching Information

Lectures

Assessment Information

2-hour written examination: 100% (all learning outcomes)

Reading and References

• Probabilistic Reasoning in Intelligent Systems, Judea Pearl, Morgan Kaufmann.

• The uncertain reasoner's companion – a mathematical perspective, Jeff Paris, Cambridge Tracts in Theoretical Computer Science.

• Modelling and Reasoning with Vague Concepts, J. Lawry, Springer

• A first course in fuzzy logic, Hung T. Nguyen and Elbert A. Walker